Nonparametric identification of linear dynamic errors-in-variables systems

Abstract

The present work handles the nonparametric identification of linear dynamic systems within an errors-in-variables framework, where the input is arbitrary and both the input and output disturbing noises are white with unknown variances. Using the property that the frequency response function and the system leakage term can be locally approximated very well by a low-order degree polynomial, a frequency domain estimator is developed, which gives consistent estimates for the frequency response function and the input-output noise variances. The consistency and uniqueness of the estimator are theoretically analyzed under mild conditions, and uncertainty bounds are also provided. The proposed method is finally validated on a simulated linear dynamic system.

Paradigm of nonstochastic approach to system identification

“…Recently EIV identification in frequency domain was considered in [14]. New results also presented in [15][16][17].…”

“…Міжнародний науково-технічний журнал Проблеми керування та інформатики, 2023, № 1 55For example, Fig.7shows the singular values of the matrix Y (15) with different noise levels. Generating system has dimension15 with 6 complex conjugate pairs and 3 real eigenvalues. The values of the invariants c p f and s p f for different modes differ up to 100 times.…”